Skills Required for Data Scientist

Overview of Skills Required for Data Scientist

In 2012, a Harvard business review stated that “Data Scientist is the sexiest job of the 21st century”. In advance to know the skills required to be a data scientist first, let us see what a data scientist does. A data scientist can be defined in many ways, but to keep it simple, let us put it this way, Data scientists can extract meaning and get valuable insights from the data. The work of a data scientist majorly involves collecting, cleaning, and manipulating data.

Technical and Non-Technical Skills

Now, let us dive into the technical and non-technical skills essential to be a data scientist.

1. Technical Skills

The technical skills required to be a data scientist are given below:

• Ability to Deal with a Large Amount of Data: The amount of data getting generated has been exponentially increasing since the last few years, and most of it is classified as unstructured data. Unstructured data is usually referred to as data that doesn’t reside in a traditional row-column database which is exactly opposite to the structured data; a few examples of unstructured data are videos, photos, audio messages. As a data scientist’s main role is to extract meaning from data, one should be comfortable dealing with large amounts of data irrespective of nature, whether it is structured or unstructured.
• Data Visualization: The data that is getting generated in the companies must be translated into a format that is easy to understand to make decisions. As a data scientist, one must be able to visualize the data with the help of tools like Tableau, Plotly, Visual.ly, D3.js, and Power BI. It is also important for a data scientist to be familiar with the principles behind visually putting the data together. This is one of the important roles for a data scientist as data visualization is the only choice for companies to work with data directly.
• Statistics: The role of statistics in data science is a very crucial one. To data scientists, statistics is the mathematical discipline that gives the necessary tools and methods to find patterns and give insights from the complex set of data by performing mathematical computations on it. As a data scientist’s role is to extract meaning by identifying patterns in the data, knowledge in statistics is a key skill for a data scientist.
• Programming Skills: With the amount of data generated 20 years ago, Excel would be enough to deal with it, but with the amount of structured and unstructured data that is generating these days’ data scientists should know programming tools like Python, R, SQL as, they give more scope to train the data set with many statistical techniques. They improve the efficiency of the process while doing data analysis.
• Data Manipulation: In most cases, the data that we need will be messy, and it will be difficult for the data scientists to work with such type of data. After getting the data from data lakes, the first step is to deal with those imperfections. Some imperfections include missing values, irregular strings like LA for Los Angeles, date formatting like 10/09/2009 and 2009/09/10. All these imperfections have to be sorted before starting the training or analysis of the data.
• Multi-Variable Calculus and Linear Algebra: Understanding Matrices’ concepts (Linear Algebra) and Differentiation (Calculus) is an important skill that a data scientist should possess. In an organization where the existing data of it plays a major role in making future predictions, small improvements in predictive performance or algorithmic optimization can make a great difference for the organization. In the initial stages of a data scientist, when using pre-coded models, one need not have an in-depth understanding of matrices or calculus. Still, to understand what is happening under the hood of models or to build out their own implementations, it is definitely necessary to understand these concepts.

2. Non -Technical Skills

The non-technical skills required to be a data scientist are given below:

• Intellectual Curiosity: While analyzing an organization’s data in most cases, no one will be able to see direct results or answers. More the questions you start putting yourself, the more the answers you will figure out from the data. In general, curiosity is defined as a strong desire to understand something. That is the reason why intellectual curiosity is an essential trait of a data scientist.
• Strong Business Acumen: Without the understanding of the organization’s data or the elements in the business model, all the technical skills that a data scientist possesses will not be able to get the required results for the organization because he will not be able to understand which features are present in the dataset should be given priority and which should be considered last. For a scientist, under understand, organization’s business model and data will help solve the potential challenges to sustain and grow its business.
• Strong Communication Skills: As a data scientist, one should prepare a presentation about their technical findings and present it to the non-technical teams like sales departments at some time or another in their career. As a data scientist, one should possess skills like storytelling (ability to tell stories from the findings) because the whole amount of time and energy spent on doing data exploration, applying statistical techniques, finding out the results, and all other things will go in vain if a data scientist is not able to convey the messages properly to business executives. And in most cases, business executives will not be interested in listening to all the steps we have followed to arrive at the conclusions; they will be mainly focused on the outcome and values presented. So it is always a best practice to keep the story crisp and on point.

Conclusion – Skills Required for Data Scientist

These are some of the most important skills that a person should possess to be a data scientist. Their main work involves working on an organization’s data, analyzing it, and presenting it to business executives.

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